Overview

Dataset statistics

Number of variables43
Number of observations12323
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 MiB
Average record size in memory344.0 B

Variable types

Categorical28
Numeric14
DateTime1

Warnings

lifecycle:transition has constant value "complete" Constant
case has a high cardinality: 776 distinct values High cardinality
hour is highly correlated with timesincemidnightHigh correlation
timesincemidnight is highly correlated with hourHigh correlation
SIRSCritHeartRate is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
DiagnosticSputum is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
DiagnosticBlood is highly correlated with DiagnosticLiquor and 4 other fieldsHigh correlation
InfectionSuspected is highly correlated with SIRSCriteria2OrMore and 4 other fieldsHigh correlation
SIRSCriteria2OrMore is highly correlated with InfectionSuspected and 4 other fieldsHigh correlation
DiagnosticLiquor is highly correlated with SIRSCritHeartRate and 22 other fieldsHigh correlation
concept:name is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
SIRSCritTemperature is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
Infusion is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
Oligurie is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
Hypoxie is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
DiagnosticArtAstrup is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
DiagnosticUrinarySediment is highly correlated with DiagnosticLiquor and 3 other fieldsHigh correlation
Hypotensie is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
lifecycle:transition is highly correlated with SIRSCritHeartRate and 25 other fieldsHigh correlation
Diagnose is highly correlated with lifecycle:transitionHigh correlation
DiagnosticUrinaryCulture is highly correlated with DiagnosticLiquor and 3 other fieldsHigh correlation
SIRSCritLeucos is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
org:group is highly correlated with lifecycle:transitionHigh correlation
DiagnosticIC is highly correlated with DiagnosticBlood and 6 other fieldsHigh correlation
label is highly correlated with lifecycle:transitionHigh correlation
DiagnosticOther is highly correlated with SIRSCritHeartRate and 22 other fieldsHigh correlation
DiagnosticECG is highly correlated with DiagnosticLiquor and 3 other fieldsHigh correlation
DisfuncOrg is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
DiagnosticLacticAcid is highly correlated with DiagnosticBlood and 4 other fieldsHigh correlation
DiagnosticXthorax is highly correlated with DiagnosticLiquor and 3 other fieldsHigh correlation
SIRSCritTachypnea is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
timesincelast is highly skewed (γ1 = 39.89537037) Skewed
Leucocytes has 3068 (24.9%) zeros Zeros
CRP has 3405 (27.6%) zeros Zeros
LacticAcid has 3978 (32.3%) zeros Zeros
weekday has 1868 (15.2%) zeros Zeros
hour has 186 (1.5%) zeros Zeros
timesincelast has 4747 (38.5%) zeros Zeros
timesincestart has 794 (6.4%) zeros Zeros

Reproduction

Analysis started2021-03-23 07:52:52.799718
Analysis finished2021-03-23 07:53:26.441874
Duration33.64 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

InfectionSuspected
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
True
 
688
False
 
88

Length

Max length5
Median length5
Mean length4.944169439
Min length4

Characters and Unicode

Total characters60927
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
True688
 
5.6%
False88
 
0.7%
2021-03-23T08:53:26.576599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:26.633757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
true688
 
5.6%
false88
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e12323
20.2%
r12235
20.1%
o11547
19.0%
t11547
19.0%
h11547
19.0%
T688
 
1.1%
u688
 
1.1%
F88
 
0.1%
a88
 
0.1%
l88
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60151
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.5%
r12235
20.3%
o11547
19.2%
t11547
19.2%
h11547
19.2%
u688
 
1.1%
a88
 
0.1%
l88
 
0.1%
s88
 
0.1%
ValueCountFrequency (%)
T688
88.7%
F88
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Latin60927
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.2%
r12235
20.1%
o11547
19.0%
t11547
19.0%
h11547
19.0%
T688
 
1.1%
u688
 
1.1%
F88
 
0.1%
a88
 
0.1%
l88
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII60927
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.2%
r12235
20.1%
o11547
19.0%
t11547
19.0%
h11547
19.0%
T688
 
1.1%
u688
 
1.1%
F88
 
0.1%
a88
 
0.1%
l88
 
0.1%

org:group
Categorical

HIGH CORRELATION

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
B
7319 
A
2719 
C
777 
F
 
210
O
 
184
Other values (20)
1114 

Length

Max length5
Median length1
Mean length1.000649193
Min length1

Characters and Unicode

Total characters12331
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowB
4th rowB
5th rowC
ValueCountFrequency (%)
B7319
59.4%
A2719
 
22.1%
C777
 
6.3%
F210
 
1.7%
O184
 
1.5%
G137
 
1.1%
L130
 
1.1%
I124
 
1.0%
E105
 
0.9%
M82
 
0.7%
Other values (15)536
 
4.3%
2021-03-23T08:53:26.806117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b7319
59.4%
a2719
 
22.1%
c777
 
6.3%
f210
 
1.7%
o184
 
1.5%
g137
 
1.1%
l130
 
1.1%
i124
 
1.0%
e105
 
0.9%
m82
 
0.7%
Other values (15)536
 
4.3%

Most occurring characters

ValueCountFrequency (%)
B7319
59.4%
A2719
 
22.1%
C777
 
6.3%
F210
 
1.7%
O184
 
1.5%
G137
 
1.1%
L130
 
1.1%
I124
 
1.0%
E105
 
0.9%
M82
 
0.7%
Other values (19)544
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter12305
99.8%
Other Punctuation16
 
0.1%
Lowercase Letter10
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
B7319
59.5%
A2719
 
22.1%
C777
 
6.3%
F210
 
1.7%
O184
 
1.5%
G137
 
1.1%
L130
 
1.1%
I124
 
1.0%
E105
 
0.9%
M82
 
0.7%
Other values (13)518
 
4.2%
ValueCountFrequency (%)
o2
20.0%
t2
20.0%
h2
20.0%
e2
20.0%
r2
20.0%
ValueCountFrequency (%)
?16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12315
99.9%
Common16
 
0.1%

Most frequent character per script

ValueCountFrequency (%)
B7319
59.4%
A2719
 
22.1%
C777
 
6.3%
F210
 
1.7%
O184
 
1.5%
G137
 
1.1%
L130
 
1.1%
I124
 
1.0%
E105
 
0.9%
M82
 
0.7%
Other values (18)528
 
4.3%
ValueCountFrequency (%)
?16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12331
100.0%

Most frequent character per block

ValueCountFrequency (%)
B7319
59.4%
A2719
 
22.1%
C777
 
6.3%
F210
 
1.7%
O184
 
1.5%
G137
 
1.1%
L130
 
1.1%
I124
 
1.0%
E105
 
0.9%
M82
 
0.7%
Other values (19)544
 
4.4%

DiagnosticBlood
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
True
 
669
False
 
107

Length

Max length5
Median length5
Mean length4.945711272
Min length4

Characters and Unicode

Total characters60946
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
True669
 
5.4%
False107
 
0.9%
2021-03-23T08:53:26.986786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:27.043416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
true669
 
5.4%
false107
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e12323
20.2%
r12216
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T669
 
1.1%
u669
 
1.1%
F107
 
0.2%
a107
 
0.2%
l107
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60170
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.5%
r12216
20.3%
o11547
19.2%
t11547
19.2%
h11547
19.2%
u669
 
1.1%
a107
 
0.2%
l107
 
0.2%
s107
 
0.2%
ValueCountFrequency (%)
T669
86.2%
F107
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin60946
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.2%
r12216
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T669
 
1.1%
u669
 
1.1%
F107
 
0.2%
a107
 
0.2%
l107
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII60946
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.2%
r12216
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T669
 
1.1%
u669
 
1.1%
F107
 
0.2%
a107
 
0.2%
l107
 
0.2%

DisfuncOrg
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
False
 
720
True
 
56

Length

Max length5
Median length5
Mean length4.995455652
Min length4

Characters and Unicode

Total characters61559
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
False720
 
5.8%
True56
 
0.5%
2021-03-23T08:53:27.199151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:27.255701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
false720
 
5.8%
true56
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e12323
20.0%
r11603
18.8%
o11547
18.8%
t11547
18.8%
h11547
18.8%
F720
 
1.2%
a720
 
1.2%
l720
 
1.2%
s720
 
1.2%
T56
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60783
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.3%
r11603
19.1%
o11547
19.0%
t11547
19.0%
h11547
19.0%
a720
 
1.2%
l720
 
1.2%
s720
 
1.2%
u56
 
0.1%
ValueCountFrequency (%)
F720
92.8%
T56
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Latin61559
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.0%
r11603
18.8%
o11547
18.8%
t11547
18.8%
h11547
18.8%
F720
 
1.2%
a720
 
1.2%
l720
 
1.2%
s720
 
1.2%
T56
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII61559
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.0%
r11603
18.8%
o11547
18.8%
t11547
18.8%
h11547
18.8%
F720
 
1.2%
a720
 
1.2%
l720
 
1.2%
s720
 
1.2%
T56
 
0.1%

SIRSCritTachypnea
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
True
 
494
False
 
282

Length

Max length5
Median length5
Mean length4.959912359
Min length4

Characters and Unicode

Total characters61121
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
True494
 
4.0%
False282
 
2.3%
2021-03-23T08:53:27.411289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:27.468019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
true494
 
4.0%
false282
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e12323
20.2%
r12041
19.7%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T494
 
0.8%
u494
 
0.8%
F282
 
0.5%
a282
 
0.5%
l282
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60345
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.4%
r12041
20.0%
o11547
19.1%
t11547
19.1%
h11547
19.1%
u494
 
0.8%
a282
 
0.5%
l282
 
0.5%
s282
 
0.5%
ValueCountFrequency (%)
T494
63.7%
F282
36.3%

Most occurring scripts

ValueCountFrequency (%)
Latin61121
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.2%
r12041
19.7%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T494
 
0.8%
u494
 
0.8%
F282
 
0.5%
a282
 
0.5%
l282
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII61121
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.2%
r12041
19.7%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T494
 
0.8%
u494
 
0.8%
F282
 
0.5%
a282
 
0.5%
l282
 
0.5%

Hypotensie
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
False
 
725
True
 
51

Length

Max length5
Median length5
Mean length4.995861397
Min length4

Characters and Unicode

Total characters61564
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
False725
 
5.9%
True51
 
0.4%
2021-03-23T08:53:27.624122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:27.680955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
false725
 
5.9%
true51
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e12323
20.0%
r11598
18.8%
o11547
18.8%
t11547
18.8%
h11547
18.8%
F725
 
1.2%
a725
 
1.2%
l725
 
1.2%
s725
 
1.2%
T51
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60788
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.3%
r11598
19.1%
o11547
19.0%
t11547
19.0%
h11547
19.0%
a725
 
1.2%
l725
 
1.2%
s725
 
1.2%
u51
 
0.1%
ValueCountFrequency (%)
F725
93.4%
T51
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Latin61564
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.0%
r11598
18.8%
o11547
18.8%
t11547
18.8%
h11547
18.8%
F725
 
1.2%
a725
 
1.2%
l725
 
1.2%
s725
 
1.2%
T51
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII61564
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.0%
r11598
18.8%
o11547
18.8%
t11547
18.8%
h11547
18.8%
F725
 
1.2%
a725
 
1.2%
l725
 
1.2%
s725
 
1.2%
T51
 
0.1%

SIRSCritHeartRate
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
True
 
645
False
 
131

Length

Max length5
Median length5
Mean length4.947658849
Min length4

Characters and Unicode

Total characters60970
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
True645
 
5.2%
False131
 
1.1%
2021-03-23T08:53:27.836791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:27.893652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
true645
 
5.2%
false131
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e12323
20.2%
r12192
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T645
 
1.1%
u645
 
1.1%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60194
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.5%
r12192
20.3%
o11547
19.2%
t11547
19.2%
h11547
19.2%
u645
 
1.1%
a131
 
0.2%
l131
 
0.2%
s131
 
0.2%
ValueCountFrequency (%)
T645
83.1%
F131
 
16.9%

Most occurring scripts

ValueCountFrequency (%)
Latin60970
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.2%
r12192
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T645
 
1.1%
u645
 
1.1%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII60970
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.2%
r12192
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T645
 
1.1%
u645
 
1.1%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%

Infusion
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
True
 
656
False
 
120

Length

Max length5
Median length5
Mean length4.94676621
Min length4

Characters and Unicode

Total characters60959
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
True656
 
5.3%
False120
 
1.0%
2021-03-23T08:53:28.049884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:28.107722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
true656
 
5.3%
false120
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e12323
20.2%
r12203
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T656
 
1.1%
u656
 
1.1%
F120
 
0.2%
a120
 
0.2%
l120
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60183
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.5%
r12203
20.3%
o11547
19.2%
t11547
19.2%
h11547
19.2%
u656
 
1.1%
a120
 
0.2%
l120
 
0.2%
s120
 
0.2%
ValueCountFrequency (%)
T656
84.5%
F120
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
Latin60959
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.2%
r12203
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T656
 
1.1%
u656
 
1.1%
F120
 
0.2%
a120
 
0.2%
l120
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII60959
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.2%
r12203
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T656
 
1.1%
u656
 
1.1%
F120
 
0.2%
a120
 
0.2%
l120
 
0.2%

DiagnosticArtAstrup
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
False
 
534
True
 
242

Length

Max length5
Median length5
Mean length4.980361925
Min length4

Characters and Unicode

Total characters61373
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
False534
 
4.3%
True242
 
2.0%
2021-03-23T08:53:28.264197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:28.321004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
false534
 
4.3%
true242
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e12323
20.1%
r11789
19.2%
o11547
18.8%
t11547
18.8%
h11547
18.8%
F534
 
0.9%
a534
 
0.9%
l534
 
0.9%
s534
 
0.9%
T242
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60597
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.3%
r11789
19.5%
o11547
19.1%
t11547
19.1%
h11547
19.1%
a534
 
0.9%
l534
 
0.9%
s534
 
0.9%
u242
 
0.4%
ValueCountFrequency (%)
F534
68.8%
T242
31.2%

Most occurring scripts

ValueCountFrequency (%)
Latin61373
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.1%
r11789
19.2%
o11547
18.8%
t11547
18.8%
h11547
18.8%
F534
 
0.9%
a534
 
0.9%
l534
 
0.9%
s534
 
0.9%
T242
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII61373
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.1%
r11789
19.2%
o11547
18.8%
t11547
18.8%
h11547
18.8%
F534
 
0.9%
a534
 
0.9%
l534
 
0.9%
s534
 
0.9%
T242
 
0.4%

concept:name
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
Leucocytes
3070 
CRP
2973 
LacticAcid
1276 
Admission NC
1145 
ER Triage
777 
Other values (9)
3082 

Length

Max length16
Median length10
Mean length9.304309016
Min length3

Characters and Unicode

Total characters114657
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowER Registration
2nd rowLeucocytes
3rd rowCRP
4th rowLacticAcid
5th rowER Triage
ValueCountFrequency (%)
Leucocytes3070
24.9%
CRP2973
24.1%
LacticAcid1276
10.4%
Admission NC1145
 
9.3%
ER Triage777
 
6.3%
ER Registration776
 
6.3%
ER Sepsis Triage775
 
6.3%
IV Antibiotics678
 
5.5%
IV Liquid620
 
5.0%
Admission IC112
 
0.9%
Other values (4)121
 
1.0%
2021-03-23T08:53:28.480806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
leucocytes3070
17.0%
crp2973
16.4%
er2344
12.9%
triage1552
8.6%
iv1298
7.2%
lacticacid1276
7.0%
admission1257
6.9%
nc1145
 
6.3%
registration776
 
4.3%
sepsis775
 
4.3%
Other values (8)1636
9.0%

Most occurring characters

ValueCountFrequency (%)
i12219
 
10.7%
c10646
 
9.3%
e9574
 
8.4%
s8693
 
7.6%
t7270
 
6.3%
R6214
 
5.4%
o5781
 
5.0%
5779
 
5.0%
L4966
 
4.3%
C4255
 
3.7%
Other values (22)39260
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter78655
68.6%
Uppercase Letter30223
 
26.4%
Space Separator5779
 
5.0%

Most frequent character per category

ValueCountFrequency (%)
i12219
15.5%
c10646
13.5%
e9574
12.2%
s8693
11.1%
t7270
9.2%
o5781
7.3%
a3709
 
4.7%
u3706
 
4.7%
d3153
 
4.0%
y3070
 
3.9%
Other values (8)10834
13.8%
ValueCountFrequency (%)
R6214
20.6%
L4966
16.4%
C4255
14.1%
A3211
10.6%
P2973
9.8%
E2344
 
7.8%
T1552
 
5.1%
I1410
 
4.7%
V1298
 
4.3%
N1145
 
3.8%
Other values (3)855
 
2.8%
ValueCountFrequency (%)
5779
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin108878
95.0%
Common5779
 
5.0%

Most frequent character per script

ValueCountFrequency (%)
i12219
 
11.2%
c10646
 
9.8%
e9574
 
8.8%
s8693
 
8.0%
t7270
 
6.7%
R6214
 
5.7%
o5781
 
5.3%
L4966
 
4.6%
C4255
 
3.9%
a3709
 
3.4%
Other values (21)35551
32.7%
ValueCountFrequency (%)
5779
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII114657
100.0%

Most frequent character per block

ValueCountFrequency (%)
i12219
 
10.7%
c10646
 
9.3%
e9574
 
8.4%
s8693
 
7.6%
t7270
 
6.3%
R6214
 
5.4%
o5781
 
5.0%
5779
 
5.0%
L4966
 
4.3%
C4255
 
3.7%
Other values (22)39260
34.2%

Age
Real number (ℝ≥0)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.96624199
Minimum20
Maximum90
Zeros0
Zeros (%)0.0%
Memory size96.4 KiB
2021-03-23T08:53:28.561851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q165
median75
Q385
95-th percentile90
Maximum90
Range70
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.0965565
Coefficient of variation (CV)0.2097727502
Kurtosis0.4015898447
Mean71.96624199
Median Absolute Deviation (MAD)10
Skewness-0.9256083407
Sum886840
Variance227.9060182
MonotocityNot monotonic
2021-03-23T08:53:28.640635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
851798
14.6%
901792
14.5%
801791
14.5%
751590
12.9%
701422
11.5%
65911
7.4%
60873
7.1%
55739
6.0%
50495
 
4.0%
40278
 
2.3%
Other values (5)634
 
5.1%
ValueCountFrequency (%)
2035
 
0.3%
2580
 
0.6%
3093
 
0.8%
35211
1.7%
40278
2.3%
ValueCountFrequency (%)
901792
14.5%
851798
14.6%
801791
14.5%
751590
12.9%
701422
11.5%

DiagnosticIC
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
True
 
686
False
 
90

Length

Max length5
Median length5
Mean length4.944331737
Min length4

Characters and Unicode

Total characters60929
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
True686
 
5.6%
False90
 
0.7%
2021-03-23T08:53:28.823300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:28.880348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
true686
 
5.6%
false90
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e12323
20.2%
r12233
20.1%
o11547
19.0%
t11547
19.0%
h11547
19.0%
T686
 
1.1%
u686
 
1.1%
F90
 
0.1%
a90
 
0.1%
l90
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60153
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.5%
r12233
20.3%
o11547
19.2%
t11547
19.2%
h11547
19.2%
u686
 
1.1%
a90
 
0.1%
l90
 
0.1%
s90
 
0.1%
ValueCountFrequency (%)
T686
88.4%
F90
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Latin60929
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.2%
r12233
20.1%
o11547
19.0%
t11547
19.0%
h11547
19.0%
T686
 
1.1%
u686
 
1.1%
F90
 
0.1%
a90
 
0.1%
l90
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII60929
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.2%
r12233
20.1%
o11547
19.0%
t11547
19.0%
h11547
19.0%
T686
 
1.1%
u686
 
1.1%
F90
 
0.1%
a90
 
0.1%
l90
 
0.1%

DiagnosticSputum
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
False
 
753
True
 
23

Length

Max length5
Median length5
Mean length4.998133571
Min length4

Characters and Unicode

Total characters61592
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
False753
 
6.1%
True23
 
0.2%
2021-03-23T08:53:29.532109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:29.590117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
false753
 
6.1%
true23
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e12323
20.0%
r11570
18.8%
o11547
18.7%
t11547
18.7%
h11547
18.7%
F753
 
1.2%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
T23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60816
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.3%
r11570
19.0%
o11547
19.0%
t11547
19.0%
h11547
19.0%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
u23
 
< 0.1%
ValueCountFrequency (%)
F753
97.0%
T23
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin61592
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.0%
r11570
18.8%
o11547
18.7%
t11547
18.7%
h11547
18.7%
F753
 
1.2%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
T23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII61592
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.0%
r11570
18.8%
o11547
18.7%
t11547
18.7%
h11547
18.7%
F753
 
1.2%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
T23
 
< 0.1%

DiagnosticLiquor
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11552 
False
 
771

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters61615
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11552
93.7%
False771
 
6.3%
2021-03-23T08:53:29.738635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:29.793278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11552
93.7%
false771
 
6.3%

Most occurring characters

ValueCountFrequency (%)
e12323
20.0%
o11552
18.7%
t11552
18.7%
h11552
18.7%
r11552
18.7%
F771
 
1.3%
a771
 
1.3%
l771
 
1.3%
s771
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60844
98.7%
Uppercase Letter771
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.3%
o11552
19.0%
t11552
19.0%
h11552
19.0%
r11552
19.0%
a771
 
1.3%
l771
 
1.3%
s771
 
1.3%
ValueCountFrequency (%)
F771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin61615
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.0%
o11552
18.7%
t11552
18.7%
h11552
18.7%
r11552
18.7%
F771
 
1.3%
a771
 
1.3%
l771
 
1.3%
s771
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII61615
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.0%
o11552
18.7%
t11552
18.7%
h11552
18.7%
r11552
18.7%
F771
 
1.3%
a771
 
1.3%
l771
 
1.3%
s771
 
1.3%

DiagnosticOther
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11551 
False
 
772

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters61615
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11551
93.7%
False772
 
6.3%
2021-03-23T08:53:29.932582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:29.986733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11551
93.7%
false772
 
6.3%

Most occurring characters

ValueCountFrequency (%)
e12323
20.0%
o11551
18.7%
t11551
18.7%
h11551
18.7%
r11551
18.7%
F772
 
1.3%
a772
 
1.3%
l772
 
1.3%
s772
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60843
98.7%
Uppercase Letter772
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.3%
o11551
19.0%
t11551
19.0%
h11551
19.0%
r11551
19.0%
a772
 
1.3%
l772
 
1.3%
s772
 
1.3%
ValueCountFrequency (%)
F772
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin61615
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.0%
o11551
18.7%
t11551
18.7%
h11551
18.7%
r11551
18.7%
F772
 
1.3%
a772
 
1.3%
l772
 
1.3%
s772
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII61615
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.0%
o11551
18.7%
t11551
18.7%
h11551
18.7%
r11551
18.7%
F772
 
1.3%
a772
 
1.3%
l772
 
1.3%
s772
 
1.3%

SIRSCriteria2OrMore
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
True
 
690
False
 
86

Length

Max length5
Median length5
Mean length4.944007141
Min length4

Characters and Unicode

Total characters60925
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
True690
 
5.6%
False86
 
0.7%
2021-03-23T08:53:30.139401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:30.197548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
true690
 
5.6%
false86
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e12323
20.2%
r12237
20.1%
o11547
19.0%
t11547
19.0%
h11547
19.0%
T690
 
1.1%
u690
 
1.1%
F86
 
0.1%
a86
 
0.1%
l86
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60149
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.5%
r12237
20.3%
o11547
19.2%
t11547
19.2%
h11547
19.2%
u690
 
1.1%
a86
 
0.1%
l86
 
0.1%
s86
 
0.1%
ValueCountFrequency (%)
T690
88.9%
F86
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin60925
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.2%
r12237
20.1%
o11547
19.0%
t11547
19.0%
h11547
19.0%
T690
 
1.1%
u690
 
1.1%
F86
 
0.1%
a86
 
0.1%
l86
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII60925
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.2%
r12237
20.1%
o11547
19.0%
t11547
19.0%
h11547
19.0%
T690
 
1.1%
u690
 
1.1%
F86
 
0.1%
a86
 
0.1%
l86
 
0.1%

DiagnosticXthorax
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
True
 
637
False
 
139

Length

Max length5
Median length5
Mean length4.948308042
Min length4

Characters and Unicode

Total characters60978
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
True637
 
5.2%
False139
 
1.1%
2021-03-23T08:53:30.357281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:30.415994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
true637
 
5.2%
false139
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e12323
20.2%
r12184
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T637
 
1.0%
u637
 
1.0%
F139
 
0.2%
a139
 
0.2%
l139
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60202
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.5%
r12184
20.2%
o11547
19.2%
t11547
19.2%
h11547
19.2%
u637
 
1.1%
a139
 
0.2%
l139
 
0.2%
s139
 
0.2%
ValueCountFrequency (%)
T637
82.1%
F139
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Latin60978
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.2%
r12184
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T637
 
1.0%
u637
 
1.0%
F139
 
0.2%
a139
 
0.2%
l139
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII60978
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.2%
r12184
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T637
 
1.0%
u637
 
1.0%
F139
 
0.2%
a139
 
0.2%
l139
 
0.2%

SIRSCritTemperature
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
True
 
645
False
 
131

Length

Max length5
Median length5
Mean length4.947658849
Min length4

Characters and Unicode

Total characters60970
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
True645
 
5.2%
False131
 
1.1%
2021-03-23T08:53:30.576239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:30.634857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
true645
 
5.2%
false131
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e12323
20.2%
r12192
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T645
 
1.1%
u645
 
1.1%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60194
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.5%
r12192
20.3%
o11547
19.2%
t11547
19.2%
h11547
19.2%
u645
 
1.1%
a131
 
0.2%
l131
 
0.2%
s131
 
0.2%
ValueCountFrequency (%)
T645
83.1%
F131
 
16.9%

Most occurring scripts

ValueCountFrequency (%)
Latin60970
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.2%
r12192
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T645
 
1.1%
u645
 
1.1%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII60970
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.2%
r12192
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T645
 
1.1%
u645
 
1.1%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%
Distinct7172
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
Minimum2013-11-07 07:18:29+00:00
Maximum2015-03-06 07:00:00+00:00
2021-03-23T08:53:30.709844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:30.810737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DiagnosticUrinaryCulture
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
True
 
388
False
 
388

Length

Max length5
Median length5
Mean length4.968514161
Min length4

Characters and Unicode

Total characters61227
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
True388
 
3.1%
False388
 
3.1%
2021-03-23T08:53:30.999761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:31.057137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
true388
 
3.1%
false388
 
3.1%

Most occurring characters

ValueCountFrequency (%)
e12323
20.1%
r11935
19.5%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T388
 
0.6%
u388
 
0.6%
F388
 
0.6%
a388
 
0.6%
l388
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60451
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.4%
r11935
19.7%
o11547
19.1%
t11547
19.1%
h11547
19.1%
u388
 
0.6%
a388
 
0.6%
l388
 
0.6%
s388
 
0.6%
ValueCountFrequency (%)
T388
50.0%
F388
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin61227
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.1%
r11935
19.5%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T388
 
0.6%
u388
 
0.6%
F388
 
0.6%
a388
 
0.6%
l388
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII61227
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.1%
r11935
19.5%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T388
 
0.6%
u388
 
0.6%
F388
 
0.6%
a388
 
0.6%
l388
 
0.6%

SIRSCritLeucos
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
False
 
735
True
 
41

Length

Max length5
Median length5
Mean length4.996672888
Min length4

Characters and Unicode

Total characters61574
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
False735
 
6.0%
True41
 
0.3%
2021-03-23T08:53:31.217924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:31.276138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
false735
 
6.0%
true41
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e12323
20.0%
r11588
18.8%
o11547
18.8%
t11547
18.8%
h11547
18.8%
F735
 
1.2%
a735
 
1.2%
l735
 
1.2%
s735
 
1.2%
T41
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60798
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.3%
r11588
19.1%
o11547
19.0%
t11547
19.0%
h11547
19.0%
a735
 
1.2%
l735
 
1.2%
s735
 
1.2%
u41
 
0.1%
ValueCountFrequency (%)
F735
94.7%
T41
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Latin61574
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.0%
r11588
18.8%
o11547
18.8%
t11547
18.8%
h11547
18.8%
F735
 
1.2%
a735
 
1.2%
l735
 
1.2%
s735
 
1.2%
T41
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII61574
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.0%
r11588
18.8%
o11547
18.8%
t11547
18.8%
h11547
18.8%
F735
 
1.2%
a735
 
1.2%
l735
 
1.2%
s735
 
1.2%
T41
 
0.1%

Oligurie
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
False
 
753
True
 
23

Length

Max length5
Median length5
Mean length4.998133571
Min length4

Characters and Unicode

Total characters61592
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
False753
 
6.1%
True23
 
0.2%
2021-03-23T08:53:31.435512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:31.493491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
false753
 
6.1%
true23
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e12323
20.0%
r11570
18.8%
o11547
18.7%
t11547
18.7%
h11547
18.7%
F753
 
1.2%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
T23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60816
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.3%
r11570
19.0%
o11547
19.0%
t11547
19.0%
h11547
19.0%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
u23
 
< 0.1%
ValueCountFrequency (%)
F753
97.0%
T23
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin61592
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.0%
r11570
18.8%
o11547
18.7%
t11547
18.7%
h11547
18.7%
F753
 
1.2%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
T23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII61592
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.0%
r11570
18.8%
o11547
18.7%
t11547
18.7%
h11547
18.7%
F753
 
1.2%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
T23
 
< 0.1%

DiagnosticLacticAcid
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
True
 
654
False
 
122

Length

Max length5
Median length5
Mean length4.946928508
Min length4

Characters and Unicode

Total characters60961
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
True654
 
5.3%
False122
 
1.0%
2021-03-23T08:53:31.652709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:31.710704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
true654
 
5.3%
false122
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e12323
20.2%
r12201
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T654
 
1.1%
u654
 
1.1%
F122
 
0.2%
a122
 
0.2%
l122
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60185
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.5%
r12201
20.3%
o11547
19.2%
t11547
19.2%
h11547
19.2%
u654
 
1.1%
a122
 
0.2%
l122
 
0.2%
s122
 
0.2%
ValueCountFrequency (%)
T654
84.3%
F122
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
Latin60961
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.2%
r12201
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T654
 
1.1%
u654
 
1.1%
F122
 
0.2%
a122
 
0.2%
l122
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII60961
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.2%
r12201
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T654
 
1.1%
u654
 
1.1%
F122
 
0.2%
a122
 
0.2%
l122
 
0.2%

lifecycle:transition
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
complete
12323 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters98584
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcomplete
2nd rowcomplete
3rd rowcomplete
4th rowcomplete
5th rowcomplete
ValueCountFrequency (%)
complete12323
100.0%
2021-03-23T08:53:31.854322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:31.907348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
complete12323
100.0%

Most occurring characters

ValueCountFrequency (%)
e24646
25.0%
c12323
12.5%
o12323
12.5%
m12323
12.5%
p12323
12.5%
l12323
12.5%
t12323
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter98584
100.0%

Most frequent character per category

ValueCountFrequency (%)
e24646
25.0%
c12323
12.5%
o12323
12.5%
m12323
12.5%
p12323
12.5%
l12323
12.5%
t12323
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin98584
100.0%

Most frequent character per script

ValueCountFrequency (%)
e24646
25.0%
c12323
12.5%
o12323
12.5%
m12323
12.5%
p12323
12.5%
l12323
12.5%
t12323
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII98584
100.0%

Most frequent character per block

ValueCountFrequency (%)
e24646
25.0%
c12323
12.5%
o12323
12.5%
m12323
12.5%
p12323
12.5%
l12323
12.5%
t12323
12.5%

Diagnose
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11843 
C
 
143
B
 
81
E
 
64
H
 
49
Other values (7)
 
143

Length

Max length5
Median length5
Mean length4.844193784
Min length1

Characters and Unicode

Total characters59695
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowother
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11843
96.1%
C143
 
1.2%
B81
 
0.7%
E64
 
0.5%
H49
 
0.4%
G42
 
0.3%
D23
 
0.2%
K22
 
0.2%
R21
 
0.2%
Q13
 
0.1%
Other values (2)22
 
0.2%
2021-03-23T08:53:32.058553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
other11843
96.1%
c143
 
1.2%
b81
 
0.7%
e64
 
0.5%
h49
 
0.4%
g42
 
0.3%
d23
 
0.2%
k22
 
0.2%
r21
 
0.2%
q13
 
0.1%
Other values (2)22
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o11843
19.8%
t11843
19.8%
h11843
19.8%
e11843
19.8%
r11843
19.8%
C143
 
0.2%
B81
 
0.1%
E64
 
0.1%
H49
 
0.1%
G42
 
0.1%
Other values (6)101
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter59215
99.2%
Uppercase Letter480
 
0.8%

Most frequent character per category

ValueCountFrequency (%)
C143
29.8%
B81
16.9%
E64
13.3%
H49
 
10.2%
G42
 
8.8%
D23
 
4.8%
K22
 
4.6%
R21
 
4.4%
Q13
 
2.7%
S12
 
2.5%
ValueCountFrequency (%)
o11843
20.0%
t11843
20.0%
h11843
20.0%
e11843
20.0%
r11843
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin59695
100.0%

Most frequent character per script

ValueCountFrequency (%)
o11843
19.8%
t11843
19.8%
h11843
19.8%
e11843
19.8%
r11843
19.8%
C143
 
0.2%
B81
 
0.1%
E64
 
0.1%
H49
 
0.1%
G42
 
0.1%
Other values (6)101
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII59695
100.0%

Most frequent character per block

ValueCountFrequency (%)
o11843
19.8%
t11843
19.8%
h11843
19.8%
e11843
19.8%
r11843
19.8%
C143
 
0.2%
B81
 
0.1%
E64
 
0.1%
H49
 
0.1%
G42
 
0.1%
Other values (6)101
 
0.2%

Hypoxie
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
False
 
760
True
 
16

Length

Max length5
Median length5
Mean length4.998701615
Min length4

Characters and Unicode

Total characters61599
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
False760
 
6.2%
True16
 
0.1%
2021-03-23T08:53:32.243864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:32.302060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
false760
 
6.2%
true16
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e12323
20.0%
r11563
18.8%
o11547
18.7%
t11547
18.7%
h11547
18.7%
F760
 
1.2%
a760
 
1.2%
l760
 
1.2%
s760
 
1.2%
T16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60823
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.3%
r11563
19.0%
o11547
19.0%
t11547
19.0%
h11547
19.0%
a760
 
1.2%
l760
 
1.2%
s760
 
1.2%
u16
 
< 0.1%
ValueCountFrequency (%)
F760
97.9%
T16
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin61599
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.0%
r11563
18.8%
o11547
18.7%
t11547
18.7%
h11547
18.7%
F760
 
1.2%
a760
 
1.2%
l760
 
1.2%
s760
 
1.2%
T16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII61599
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.0%
r11563
18.8%
o11547
18.7%
t11547
18.7%
h11547
18.7%
F760
 
1.2%
a760
 
1.2%
l760
 
1.2%
s760
 
1.2%
T16
 
< 0.1%

DiagnosticUrinarySediment
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
True
 
423
False
 
353

Length

Max length5
Median length5
Mean length4.965673943
Min length4

Characters and Unicode

Total characters61192
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
True423
 
3.4%
False353
 
2.9%
2021-03-23T08:53:32.461591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:32.520036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
true423
 
3.4%
false353
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e12323
20.1%
r11970
19.6%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T423
 
0.7%
u423
 
0.7%
F353
 
0.6%
a353
 
0.6%
l353
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60416
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.4%
r11970
19.8%
o11547
19.1%
t11547
19.1%
h11547
19.1%
u423
 
0.7%
a353
 
0.6%
l353
 
0.6%
s353
 
0.6%
ValueCountFrequency (%)
T423
54.5%
F353
45.5%

Most occurring scripts

ValueCountFrequency (%)
Latin61192
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.1%
r11970
19.6%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T423
 
0.7%
u423
 
0.7%
F353
 
0.6%
a353
 
0.6%
l353
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII61192
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.1%
r11970
19.6%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T423
 
0.7%
u423
 
0.7%
F353
 
0.6%
a353
 
0.6%
l353
 
0.6%

DiagnosticECG
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
other
11547 
True
 
623
False
 
153

Length

Max length5
Median length5
Mean length4.949444129
Min length4

Characters and Unicode

Total characters60992
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other11547
93.7%
True623
 
5.1%
False153
 
1.2%
2021-03-23T08:53:32.680960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:32.740829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other11547
93.7%
true623
 
5.1%
false153
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e12323
20.2%
r12170
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T623
 
1.0%
u623
 
1.0%
F153
 
0.3%
a153
 
0.3%
l153
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60216
98.7%
Uppercase Letter776
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
e12323
20.5%
r12170
20.2%
o11547
19.2%
t11547
19.2%
h11547
19.2%
u623
 
1.0%
a153
 
0.3%
l153
 
0.3%
s153
 
0.3%
ValueCountFrequency (%)
T623
80.3%
F153
 
19.7%

Most occurring scripts

ValueCountFrequency (%)
Latin60992
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
20.2%
r12170
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T623
 
1.0%
u623
 
1.0%
F153
 
0.3%
a153
 
0.3%
l153
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII60992
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
20.2%
r12170
20.0%
o11547
18.9%
t11547
18.9%
h11547
18.9%
T623
 
1.0%
u623
 
1.0%
F153
 
0.3%
a153
 
0.3%
l153
 
0.3%

case
Categorical

HIGH CARDINALITY

Distinct776
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
NGA
 
185
KM
 
168
OD
 
118
GK
 
88
YX
 
83
Other values (771)
11681 

Length

Max length7
Median length2
Mean length2.311937028
Min length1

Characters and Unicode

Total characters28490
Distinct characters31
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA
ValueCountFrequency (%)
NGA185
 
1.5%
KM168
 
1.4%
OD118
 
1.0%
GK88
 
0.7%
YX83
 
0.7%
ZMA66
 
0.5%
YLA59
 
0.5%
NZ59
 
0.5%
GF57
 
0.5%
MKA51
 
0.4%
Other values (766)11389
92.4%
2021-03-23T08:53:32.943949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nga185
 
1.5%
km168
 
1.4%
od118
 
1.0%
gk88
 
0.7%
yx83
 
0.7%
zma66
 
0.5%
nz59
 
0.5%
yla59
 
0.5%
gf57
 
0.5%
mka51
 
0.4%
Other values (766)11389
92.4%

Most occurring characters

ValueCountFrequency (%)
A4793
 
16.8%
K1367
 
4.8%
G1331
 
4.7%
M1261
 
4.4%
N1087
 
3.8%
D1079
 
3.8%
H1072
 
3.8%
L1063
 
3.7%
B1052
 
3.7%
E1034
 
3.6%
Other values (21)13351
46.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter28322
99.4%
Lowercase Letter168
 
0.6%

Most frequent character per category

ValueCountFrequency (%)
A4793
 
16.9%
K1367
 
4.8%
G1331
 
4.7%
M1261
 
4.5%
N1087
 
3.8%
D1079
 
3.8%
H1072
 
3.8%
L1063
 
3.8%
B1052
 
3.7%
E1034
 
3.7%
Other values (16)13183
46.5%
ValueCountFrequency (%)
i48
28.6%
s48
28.6%
m24
14.3%
n24
14.3%
g24
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin28490
100.0%

Most frequent character per script

ValueCountFrequency (%)
A4793
 
16.8%
K1367
 
4.8%
G1331
 
4.7%
M1261
 
4.4%
N1087
 
3.8%
D1079
 
3.8%
H1072
 
3.8%
L1063
 
3.7%
B1052
 
3.7%
E1034
 
3.6%
Other values (21)13351
46.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII28490
100.0%

Most frequent character per block

ValueCountFrequency (%)
A4793
 
16.8%
K1367
 
4.8%
G1331
 
4.7%
M1261
 
4.4%
N1087
 
3.8%
D1079
 
3.8%
H1072
 
3.8%
L1063
 
3.7%
B1052
 
3.7%
E1034
 
3.6%
Other values (21)13351
46.9%

Leucocytes
Real number (ℝ≥0)

ZEROS

Distinct354
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.09336201
Minimum0
Maximum381.3
Zeros3068
Zeros (%)24.9%
Memory size96.4 KiB
2021-03-23T08:53:33.048050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median9.3
Q314
95-th percentile24.1
Maximum381.3
Range381.3
Interquartile range (IQR)13.8

Descriptive statistics

Standard deviation13.38140832
Coefficient of variation (CV)1.325763241
Kurtosis189.765393
Mean10.09336201
Median Absolute Deviation (MAD)5.8
Skewness10.3133933
Sum124380.5
Variance179.0620887
MonotocityNot monotonic
2021-03-23T08:53:33.162067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03068
 
24.9%
11.4108
 
0.9%
10.199
 
0.8%
9.895
 
0.8%
8.794
 
0.8%
8.588
 
0.7%
1087
 
0.7%
14.587
 
0.7%
10.983
 
0.7%
6.782
 
0.7%
Other values (344)8432
68.4%
ValueCountFrequency (%)
03068
24.9%
0.214
 
0.1%
0.316
 
0.1%
0.45
 
< 0.1%
0.521
 
0.2%
ValueCountFrequency (%)
381.31
 
< 0.1%
297.61
 
< 0.1%
296.25
< 0.1%
234.21
 
< 0.1%
199.82
 
< 0.1%

CRP
Real number (ℝ≥0)

ZEROS

Distinct365
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.42457194
Minimum0
Maximum573
Zeros3405
Zeros (%)27.6%
Memory size96.4 KiB
2021-03-23T08:53:33.280222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median58
Q3140
95-th percentile281
Maximum573
Range573
Interquartile range (IQR)140

Descriptive statistics

Standard deviation95.63519552
Coefficient of variation (CV)1.10657413
Kurtosis1.21015193
Mean86.42457194
Median Absolute Deviation (MAD)58
Skewness1.238898727
Sum1065010
Variance9146.090622
MonotocityNot monotonic
2021-03-23T08:53:33.391363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03405
27.6%
8122
 
1.0%
17100
 
0.8%
987
 
0.7%
1187
 
0.7%
681
 
0.7%
2081
 
0.7%
1278
 
0.6%
1476
 
0.6%
1972
 
0.6%
Other values (355)8134
66.0%
ValueCountFrequency (%)
03405
27.6%
535
 
0.3%
681
 
0.7%
768
 
0.6%
8122
 
1.0%
ValueCountFrequency (%)
5734
< 0.1%
5165
< 0.1%
5074
< 0.1%
4783
 
< 0.1%
4778
0.1%

LacticAcid
Real number (ℝ≥0)

ZEROS

Distinct75
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.228337256
Minimum0
Maximum14.9
Zeros3978
Zeros (%)32.3%
Memory size96.4 KiB
2021-03-23T08:53:33.509204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.1
Q31.8
95-th percentile3.2
Maximum14.9
Range14.9
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.308945185
Coefficient of variation (CV)1.065623613
Kurtosis15.2603954
Mean1.228337256
Median Absolute Deviation (MAD)1
Skewness2.576755932
Sum15136.8
Variance1.713337496
MonotocityNot monotonic
2021-03-23T08:53:33.625148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03978
32.3%
1.1566
 
4.6%
1.2559
 
4.5%
1.3506
 
4.1%
1.5503
 
4.1%
1436
 
3.5%
1.4429
 
3.5%
1.9396
 
3.2%
1.6390
 
3.2%
0.9364
 
3.0%
Other values (65)4196
34.1%
ValueCountFrequency (%)
03978
32.3%
0.28
 
0.1%
0.319
 
0.2%
0.445
 
0.4%
0.591
 
0.7%
ValueCountFrequency (%)
14.99
0.1%
12.14
 
< 0.1%
105
< 0.1%
9.612
0.1%
9.54
 
< 0.1%

openCases
Real number (ℝ≥0)

Distinct93
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.05420758
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Memory size96.4 KiB
2021-03-23T08:53:33.746541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q153
median77
Q384
95-th percentile90
Maximum93
Range92
Interquartile range (IQR)31

Descriptive statistics

Standard deviation22.24670185
Coefficient of variation (CV)0.3317719
Kurtosis-0.1310341531
Mean67.05420758
Median Absolute Deviation (MAD)10
Skewness-1.01565357
Sum826309
Variance494.9157433
MonotocityNot monotonic
2021-03-23T08:53:33.851747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84601
 
4.9%
83579
 
4.7%
87570
 
4.6%
82516
 
4.2%
79472
 
3.8%
78452
 
3.7%
85450
 
3.7%
77426
 
3.5%
80379
 
3.1%
86372
 
3.0%
Other values (83)7506
60.9%
ValueCountFrequency (%)
110
0.1%
210
0.1%
311
0.1%
411
0.1%
512
0.1%
ValueCountFrequency (%)
9343
 
0.3%
92178
1.4%
91126
 
1.0%
90299
2.4%
89333
2.7%

weekday
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.961048446
Minimum0
Maximum6
Zeros1868
Zeros (%)15.2%
Memory size96.4 KiB
2021-03-23T08:53:33.938838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.012969296
Coefficient of variation (CV)0.6798164004
Kurtosis-1.249991944
Mean2.961048446
Median Absolute Deviation (MAD)2
Skewness0.03603548414
Sum36489
Variance4.052045387
MonotocityNot monotonic
2021-03-23T08:53:34.009873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
21883
15.3%
01868
15.2%
31808
14.7%
61788
14.5%
51713
13.9%
11682
13.6%
41581
12.8%
ValueCountFrequency (%)
01868
15.2%
11682
13.6%
21883
15.3%
31808
14.7%
41581
12.8%
ValueCountFrequency (%)
61788
14.5%
51713
13.9%
41581
12.8%
31808
14.7%
21883
15.3%

month
Real number (ℝ≥0)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.578511726
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size96.4 KiB
2021-03-23T08:53:34.091157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.540715366
Coefficient of variation (CV)0.5382243757
Kurtosis-1.292333202
Mean6.578511726
Median Absolute Deviation (MAD)3
Skewness-0.02007573198
Sum81067
Variance12.53666531
MonotocityNot monotonic
2021-03-23T08:53:34.167177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
111273
10.3%
81109
9.0%
51104
9.0%
121088
8.8%
21054
8.6%
11054
8.6%
101041
8.4%
41016
8.2%
31011
8.2%
9921
7.5%
Other values (2)1652
13.4%
ValueCountFrequency (%)
11054
8.6%
21054
8.6%
31011
8.2%
41016
8.2%
51104
9.0%
ValueCountFrequency (%)
121088
8.8%
111273
10.3%
101041
8.4%
9921
7.5%
81109
9.0%

hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.77148422
Minimum0
Maximum23
Zeros186
Zeros (%)1.5%
Memory size96.4 KiB
2021-03-23T08:53:34.248985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q16
median9
Q315
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.671615395
Coefficient of variation (CV)0.5265398232
Kurtosis-0.8316883218
Mean10.77148422
Median Absolute Deviation (MAD)3
Skewness0.4650590765
Sum132737
Variance32.16722119
MonotocityNot monotonic
2021-03-23T08:53:34.335407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
62092
17.0%
71591
 
12.9%
5874
 
7.1%
13566
 
4.6%
9557
 
4.5%
10550
 
4.5%
12548
 
4.4%
16510
 
4.1%
14500
 
4.1%
11466
 
3.8%
Other values (14)4069
33.0%
ValueCountFrequency (%)
0186
1.5%
1112
0.9%
2142
1.2%
3129
1.0%
4161
1.3%
ValueCountFrequency (%)
23214
1.7%
22249
2.0%
21378
3.1%
20395
3.2%
19456
3.7%

day
Real number (ℝ≥0)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.62574049
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size96.4 KiB
2021-03-23T08:53:34.425050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.016576175
Coefficient of variation (CV)0.577033529
Kurtosis-1.245249694
Mean15.62574049
Median Absolute Deviation (MAD)8
Skewness0.04050082199
Sum192556
Variance81.29864593
MonotocityNot monotonic
2021-03-23T08:53:34.513373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2529
 
4.3%
27528
 
4.3%
25487
 
4.0%
11484
 
3.9%
7481
 
3.9%
17457
 
3.7%
12439
 
3.6%
5431
 
3.5%
1431
 
3.5%
9426
 
3.5%
Other values (21)7630
61.9%
ValueCountFrequency (%)
1431
3.5%
2529
4.3%
3362
2.9%
4368
3.0%
5431
3.5%
ValueCountFrequency (%)
31295
2.4%
30366
3.0%
29392
3.2%
28368
3.0%
27528
4.3%

timesincemidnight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1319
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean664.9143066
Minimum0
Maximum1439
Zeros5
Zeros (%)< 0.1%
Memory size96.4 KiB
2021-03-23T08:53:34.616836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile264
Q1360
median585
Q3942
95-th percentile1297
Maximum1439
Range1439
Interquartile range (IQR)582

Descriptive statistics

Standard deviation348.5232983
Coefficient of variation (CV)0.5241627303
Kurtosis-0.8981855335
Mean664.9143066
Median Absolute Deviation (MAD)225
Skewness0.4642029162
Sum8193739
Variance121468.4895
MonotocityNot monotonic
2021-03-23T08:53:34.724539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3601933
 
15.7%
4201348
 
10.9%
300768
 
6.2%
540122
 
1.0%
600114
 
0.9%
780111
 
0.9%
72086
 
0.7%
84035
 
0.3%
57031
 
0.3%
108030
 
0.2%
Other values (1309)7745
62.8%
ValueCountFrequency (%)
05
< 0.1%
14
< 0.1%
33
< 0.1%
56
< 0.1%
66
< 0.1%
ValueCountFrequency (%)
14393
< 0.1%
14385
< 0.1%
14373
< 0.1%
14363
< 0.1%
14352
 
< 0.1%

timesincelast
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct3748
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37996.11653
Minimum0
Maximum14969508
Zeros4747
Zeros (%)38.5%
Memory size96.4 KiB
2021-03-23T08:53:34.840178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median96
Q313080
95-th percentile172800
Maximum14969508
Range14969508
Interquartile range (IQR)13080

Descriptive statistics

Standard deviation275409.202
Coefficient of variation (CV)7.248351336
Kurtosis1849.532913
Mean37996.11653
Median Absolute Deviation (MAD)96
Skewness39.89537037
Sum468226144
Variance7.585022856 × 1010
MonotocityNot monotonic
2021-03-23T08:53:34.948432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04747
38.5%
86400513
 
4.2%
172800308
 
2.5%
259200111
 
0.9%
34560061
 
0.5%
1557
 
0.5%
156
 
0.5%
450
 
0.4%
1647
 
0.4%
545
 
0.4%
Other values (3738)6328
51.4%
ValueCountFrequency (%)
04747
38.5%
156
 
0.5%
27
 
0.1%
326
 
0.2%
450
 
0.4%
ValueCountFrequency (%)
149695081
< 0.1%
144461031
< 0.1%
114082231
< 0.1%
107220281
< 0.1%
78765221
< 0.1%

timesincestart
Real number (ℝ≥0)

ZEROS

Distinct6460
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274393.3734
Minimum0
Maximum16065386
Zeros794
Zeros (%)6.4%
Memory size96.4 KiB
2021-03-23T08:53:35.064505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11237.5
median13583
Q3242571
95-th percentile1307207
Maximum16065386
Range16065386
Interquartile range (IQR)241333.5

Descriptive statistics

Standard deviation728179.4041
Coefficient of variation (CV)2.653779116
Kurtosis83.40614419
Mean274393.3734
Median Absolute Deviation (MAD)13583
Skewness7.191912666
Sum3381349541
Variance5.302452446 × 1011
MonotocityNot monotonic
2021-03-23T08:53:35.173985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0794
 
6.4%
113712
 
0.1%
75910
 
0.1%
61910
 
0.1%
172110
 
0.1%
156910
 
0.1%
3259
 
0.1%
13089
 
0.1%
20219
 
0.1%
15529
 
0.1%
Other values (6450)11441
92.8%
ValueCountFrequency (%)
0794
6.4%
31
 
< 0.1%
141
 
< 0.1%
161
 
< 0.1%
271
 
< 0.1%
ValueCountFrequency (%)
160653861
< 0.1%
157419551
< 0.1%
119302601
< 0.1%
112953501
< 0.1%
98809691
< 0.1%

remainingtime
Real number (ℝ≥0)

Distinct7565
Distinct (%)61.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3247909.619
Minimum0
Maximum36488789
Zeros105
Zeros (%)0.9%
Memory size96.4 KiB
2021-03-23T08:53:35.292681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile98256.6
Q1377360.5
median886500
Q33368880
95-th percentile15490103
Maximum36488789
Range36488789
Interquartile range (IQR)2991519.5

Descriptive statistics

Standard deviation5537277.65
Coefficient of variation (CV)1.704874303
Kurtosis10.02592526
Mean3247909.619
Median Absolute Deviation (MAD)633894
Skewness2.949948665
Sum4.002399024 × 1010
Variance3.066144377 × 1013
MonotocityNot monotonic
2021-03-23T08:53:35.404446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0105
 
0.9%
9360036
 
0.3%
18000030
 
0.2%
28080029
 
0.2%
27000024
 
0.2%
35640024
 
0.2%
10800023
 
0.2%
52920023
 
0.2%
26640023
 
0.2%
2160022
 
0.2%
Other values (7555)11984
97.2%
ValueCountFrequency (%)
0105
0.9%
8101
 
< 0.1%
11971
 
< 0.1%
12402
 
< 0.1%
14201
 
< 0.1%
ValueCountFrequency (%)
364887891
 
< 0.1%
364885731
 
< 0.1%
364885541
 
< 0.1%
364879383
< 0.1%
364820271
 
< 0.1%

OrderOfEvent
Real number (ℝ≥0)

Distinct185
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.0962428
Minimum1
Maximum185
Zeros0
Zeros (%)0.0%
Memory size96.4 KiB
2021-03-23T08:53:35.517421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median9
Q314
95-th percentile38
Maximum185
Range184
Interquartile range (IQR)10

Descriptive statistics

Standard deviation19.13350582
Coefficient of variation (CV)1.460991989
Kurtosis28.73685061
Mean13.0962428
Median Absolute Deviation (MAD)5
Skewness4.830673
Sum161385
Variance366.091045
MonotocityNot monotonic
2021-03-23T08:53:35.624181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1776
 
6.3%
4776
 
6.3%
2776
 
6.3%
3776
 
6.3%
5774
 
6.3%
6771
 
6.3%
7757
 
6.1%
8746
 
6.1%
9728
 
5.9%
10681
 
5.5%
Other values (175)4762
38.6%
ValueCountFrequency (%)
1776
6.3%
2776
6.3%
3776
6.3%
4776
6.3%
5774
6.3%
ValueCountFrequency (%)
1851
< 0.1%
1841
< 0.1%
1831
< 0.1%
1821
< 0.1%
1811
< 0.1%

label
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
deviant
10066 
regular
2257 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters86261
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdeviant
2nd rowdeviant
3rd rowdeviant
4th rowdeviant
5th rowdeviant
ValueCountFrequency (%)
deviant10066
81.7%
regular2257
 
18.3%
2021-03-23T08:53:35.817254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:53:35.871349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
deviant10066
81.7%
regular2257
 
18.3%

Most occurring characters

ValueCountFrequency (%)
e12323
14.3%
a12323
14.3%
d10066
11.7%
v10066
11.7%
i10066
11.7%
n10066
11.7%
t10066
11.7%
r4514
 
5.2%
g2257
 
2.6%
u2257
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter86261
100.0%

Most frequent character per category

ValueCountFrequency (%)
e12323
14.3%
a12323
14.3%
d10066
11.7%
v10066
11.7%
i10066
11.7%
n10066
11.7%
t10066
11.7%
r4514
 
5.2%
g2257
 
2.6%
u2257
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin86261
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12323
14.3%
a12323
14.3%
d10066
11.7%
v10066
11.7%
i10066
11.7%
n10066
11.7%
t10066
11.7%
r4514
 
5.2%
g2257
 
2.6%
u2257
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII86261
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12323
14.3%
a12323
14.3%
d10066
11.7%
v10066
11.7%
i10066
11.7%
n10066
11.7%
t10066
11.7%
r4514
 
5.2%
g2257
 
2.6%
u2257
 
2.6%

Interactions

2021-03-23T08:53:06.203621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:06.315280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:06.419321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:06.525162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:06.628841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:06.728261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:06.824124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:06.917429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:07.014804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:07.116373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:07.221986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:07.326314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:07.428591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:07.528796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:07.634992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:07.736124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:07.839061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:07.936452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:08.037129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:08.135522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:08.232419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:08.332992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:08.437825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:08.543756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:08.654235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:08.759622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:08.862751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:08.958706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:09.057429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:09.153595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:09.243960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:09.337510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:09.429214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:09.518807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:09.617609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:09.717214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:09.816307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:09.917153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:10.015795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:10.112472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:10.211603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:10.314328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:10.411433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:10.504725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:10.601244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:10.695848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:10.788012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:10.884994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:10.985543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:11.087070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:11.191022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:11.294437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:11.394524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:11.484560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:11.577818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:11.668522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:11.759255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:11.846730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:11.931954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:12.015304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:12.103162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:12.194793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:12.287281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:12.382209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:12.475282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:12.566705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:12.663338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:12.763662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:12.865899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:12.966870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:13.060542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:13.153275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:13.247060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:13.344036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:13.442680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:13.542216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:13.647723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:13.747218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:13.844051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:13.937073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:14.033637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:14.124810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:14.217970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:14.307873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:14.403383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:14.494506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:14.589767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:14.689408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:14.789818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:14.892731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:14.993276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:15.091803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:15.186558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:15.283763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:15.375816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:15.470286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:15.558131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:15.651967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:15.741218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:15.833671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:15.929976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:16.026671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:16.125953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:16.222728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:16.317585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:16.418782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:16.522815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:16.621105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:16.721986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:16.816417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:16.914076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:17.009911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:17.103209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:17.205518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:17.308923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:17.415937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:18.114698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:18.212486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:18.313135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:18.416926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:18.515992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:18.616409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:18.711377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:18.809406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:18.907872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:19.005596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:19.105693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:19.208121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:19.312827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:19.415860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:19.516546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:19.618346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:19.723152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:19.822814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:19.924235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:20.019629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:20.118695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:20.215636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:20.310295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:20.410717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:20.514802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:20.620617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:20.724417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:20.826685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:20.931658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:21.039974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:21.142963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:21.247667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:21.346732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:21.449289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:21.549362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:21.651070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:21.754190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:21.860724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:21.969085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:22.076360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:22.182061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:22.284097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:22.388991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:22.488847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:22.590937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:22.686656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:22.785966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:22.887608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:22.982619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:23.081530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:23.184938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:23.289048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:23.395912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:23.498846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:23.598732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:23.701660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:23.799153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:23.898664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:23.992171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:24.088956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:24.184036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:24.276546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:24.373373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:24.474536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:24.576351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:53:24.680619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-23T08:53:35.937408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-23T08:53:36.107897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-23T08:53:36.276069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-23T08:53:37.001607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-23T08:53:37.331980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-23T08:53:24.973840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-23T08:53:26.147480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

InfectionSuspectedorg:groupDiagnosticBloodDisfuncOrgSIRSCritTachypneaHypotensieSIRSCritHeartRateInfusionDiagnosticArtAstrupconcept:nameAgeDiagnosticICDiagnosticSputumDiagnosticLiquorDiagnosticOtherSIRSCriteria2OrMoreDiagnosticXthoraxSIRSCritTemperaturetime:timestampDiagnosticUrinaryCultureSIRSCritLeucosOligurieDiagnosticLacticAcidlifecycle:transitionDiagnoseHypoxieDiagnosticUrinarySedimentDiagnosticECGcaseLeucocytesCRPLacticAcidopenCasesweekdaymonthhourdaytimesincemidnighttimesincelasttimesincestartremainingtimeOrderOfEventlabel
0TrueATrueTrueTrueTrueTrueTrueTrueER Registration85.0TrueFalseFalseFalseTrueTrueTrue2014-10-22 09:15:41+00:00TrueFalseFalseTruecompleteotherFalseTrueTrueA0.00.00.081.0210.09.022.0555.00.00.0968359.01.0deviant
1otherBotherotherotherotherotherotherotherLeucocytes85.0otherotherotherotherotherotherother2014-10-22 09:27:00+00:00otherotherotherothercompleteotherotherotherotherA9.60.00.081.0210.09.022.0567.00.0679.0967680.02.0deviant
2otherBotherotherotherotherotherotherotherCRP85.0otherotherotherotherotherotherother2014-10-22 09:27:00+00:00otherotherotherothercompleteotherotherotherotherA9.621.00.081.0210.09.022.0567.00.0679.0967680.03.0deviant
3otherBotherotherotherotherotherotherotherLacticAcid85.0otherotherotherotherotherotherother2014-10-22 09:27:00+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.09.022.0567.0679.0679.0967680.04.0deviant
4otherCotherotherotherotherotherotherotherER Triage85.0otherotherotherotherotherotherother2014-10-22 09:33:37+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.09.022.0573.0397.01076.0967283.05.0deviant
5otherAotherotherotherotherotherotherotherER Sepsis Triage85.0otherotherotherotherotherotherother2014-10-22 09:34:00+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.09.022.0574.023.01099.0967260.06.0deviant
6otherAotherotherotherotherotherotherotherIV Liquid85.0otherotherotherotherotherotherother2014-10-22 12:03:47+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.012.022.0723.00.010086.0958273.07.0deviant
7otherAotherotherotherotherotherotherotherIV Antibiotics85.0otherotherotherotherotherotherother2014-10-22 12:03:47+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.012.022.0723.08987.010086.0958273.08.0deviant
8otherDotherotherotherotherotherotherotherAdmission NC85.0otherotherotherotherotherotherother2014-10-22 12:13:19+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.012.022.0733.0572.010658.0957701.09.0deviant
9otherBotherotherotherotherotherotherotherCRP85.0otherotherotherotherotherotherother2014-10-24 07:00:00+00:00otherotherotherothercompleteotherotherotherotherA9.6109.02.279.0410.07.024.0420.00.0164659.0803700.010.0deviant

Last rows

InfectionSuspectedorg:groupDiagnosticBloodDisfuncOrgSIRSCritTachypneaHypotensieSIRSCritHeartRateInfusionDiagnosticArtAstrupconcept:nameAgeDiagnosticICDiagnosticSputumDiagnosticLiquorDiagnosticOtherSIRSCriteria2OrMoreDiagnosticXthoraxSIRSCritTemperaturetime:timestampDiagnosticUrinaryCultureSIRSCritLeucosOligurieDiagnosticLacticAcidlifecycle:transitionDiagnoseHypoxieDiagnosticUrinarySedimentDiagnosticECGcaseLeucocytesCRPLacticAcidopenCasesweekdaymonthhourdaytimesincemidnighttimesincelasttimesincestartremainingtimeOrderOfEventlabel
12313otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-15 15:08:00+00:00otherotherotherothercompleteotherotherotherothermissing19.0207.01.075.0511.015.015.0908.0205680.0483761.0598920.015.0regular
12314otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-15 18:42:00+00:00otherotherotherothercompleteotherotherotherothermissing17.1207.01.076.0511.018.015.01122.012840.0496601.0586080.016.0regular
12315otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-15 22:46:00+00:00otherotherotherothercompleteotherotherotherothermissing17.7207.01.076.0511.022.015.01366.014640.0511241.0571440.017.0regular
12316otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-16 06:00:00+00:00otherotherotherothercompleteotherotherotherothermissing17.2207.01.077.0611.06.016.0360.00.0537281.0545400.018.0regular
12317otherBotherotherotherotherotherotherotherCRP90.0otherotherotherotherotherotherother2014-11-16 06:00:00+00:00otherotherotherothercompleteotherotherotherothermissing17.278.01.077.0611.06.016.0360.026040.0537281.0545400.019.0regular
12318otherBotherotherotherotherotherotherotherLacticAcid90.0otherotherotherotherotherotherother2014-11-16 12:05:00+00:00otherotherotherothercompleteotherotherotherothermissing17.278.01.775.0611.012.016.0725.021900.0559181.0523500.020.0regular
12319otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-17 06:00:00+00:00otherotherotherothercompleteotherotherotherothermissing17.478.01.777.0011.06.017.0360.00.0623681.0459000.021.0regular
12320otherBotherotherotherotherotherotherotherCRP90.0otherotherotherotherotherotherother2014-11-17 06:00:00+00:00otherotherotherothercompleteotherotherotherothermissing17.464.01.777.0011.06.017.0360.064500.0623681.0459000.022.0regular
12321otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-18 06:00:00+00:00otherotherotherothercompleteotherotherotherothermissing16.864.01.778.0111.06.018.0360.086400.0710081.0372600.023.0regular
12322otherEotherotherotherotherotherotherotherRelease C90.0otherotherotherotherotherotherother2014-11-22 13:30:00+00:00otherotherotherothercompleteotherotherotherothermissing16.864.01.770.0511.013.022.0810.0372600.01082681.00.024.0regular